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---
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: bridalMakeupClassifier
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9969230769230769
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bridalMakeupClassifier
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0326
- Accuracy: 0.9969
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1604 | 1.0 | 23 | 0.0509 | 0.9846 |
| 0.0837 | 2.0 | 46 | 0.0353 | 0.9877 |
| 0.0588 | 3.0 | 69 | 0.0326 | 0.9969 |
| 0.05 | 4.0 | 92 | 0.0302 | 0.9969 |
| 0.0284 | 5.0 | 115 | 0.0313 | 0.9938 |
| 0.0372 | 6.0 | 138 | 0.0273 | 0.9938 |
| 0.0461 | 7.0 | 161 | 0.0268 | 0.9969 |
| 0.0338 | 8.0 | 184 | 0.0259 | 0.9969 |
| 0.0253 | 9.0 | 207 | 0.0256 | 0.9938 |
| 0.0326 | 10.0 | 230 | 0.0266 | 0.9969 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.20.0
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